1 citations · 1 across the 1 of their papers we have counts for
4 papers
Continuous Control for High-Dimensional State Spaces: An Interactive Learning Approach
Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar +1
Deep Reinforcement Learning (DRL) has become a powerful methodology to solve complex decision-making problems. However, DRL has several limitations when used in real-world problems…
Deep Reinforcement Learning with Feedback-based Exploration
Jan Scholten, Daan Wout, Carlos Celemin +1
Deep Reinforcement Learning has enabled the control of increasingly complex and high-dimensional problems. However, the need of vast amounts of data before reasonable performance i…
Learning Gaussian Policies from Corrective Human Feedback
Daan Wout, Jan Scholten, Carlos Celemin +1
Learning from human feedback is a viable alternative to control design that does not require modelling or control expertise. Particularly, learning from corrective advice garners a…
Interactive Learning with Corrective Feedback for Policies based on Deep Neural Networks
Rodrigo Pérez-Dattari, Carlos Celemin, Javier Ruiz-del-Solar +1
Deep Reinforcement Learning (DRL) has become a powerful strategy to solve complex decision making problems based on Deep Neural Networks (DNNs). However, it is highly data demandin…